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Decentralized multiagent reinforcement learning algorithm using a cluster-synchronized laser network.

Shun Kotoku¹, Takatomo Mihana¹, André Röhm¹

¹The University of Tokyo, Department of Information Physics and Computing, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Physical Review. E

|February 7, 2025

Summary

This summary is machine-generated.

This study introduces a novel photonic algorithm for multiagent reinforcement learning (MARL) to solve the competitive multiarmed bandit (CMAB) problem, enabling cooperative decision-making without direct information sharing.

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Area of Science:

Physics
Computer Science
Engineering

Background:

Multiagent reinforcement learning (MARL) is vital for fields like wireless networking and autonomous driving.
The competitive multiarmed bandit (CMAB) problem is a fundamental challenge in MARL.

Purpose of the Study:

To propose a photonic-based decision-making algorithm for the CMAB problem.
To demonstrate decentralized cooperative decision-making in MARL using physical processes.

Main Methods:

Utilizing optically coupled lasers exhibiting chaotic oscillations and cluster synchronization.
Implementing a decentralized coupling adjustment algorithm.
Conducting numerical simulations to validate the approach.

Main Results:

The photonic algorithm efficiently balances exploration and exploitation in CMAB.
Cooperative decision-making is achieved without explicit information sharing among agents.
Complex physical processes controlled by simple algorithms enable decentralized reinforcement learning.

Conclusions:

Photonic systems can be leveraged for advanced MARL solutions.
Decentralized control in MARL is feasible through physical system dynamics.
The proposed method offers a novel approach to cooperative AI.